Multi-clinical index classifier combined with AI algorithm model to predict the prognosis of gallbladder cancer

Zhou, Yanzhao; Chen, Siyu; Wu, Yuchen; Li, Lanqing; Lou, Qinqin; Chen, Yongyi; Xu, Songxiao

doi:10.3389/fonc.2023.1171837

Cited by 6 publications

(5 citation statements)

References 32 publications

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“…Its low sensitivity is not suitable for MRD detection, but with the rise of AI image recognition technology, the problem of cell morphology inspection about the order of magnitude is expected to be solved to a certain extent. According to the results of the study, it was also found that when patients obtained CR before harvesting, the possibility of their samples being contaminated by tumor cells was significantly reduced [14][15]. However, for patients who obtained VGPR or PR before harvesting, there was no statistical significance in the positive proportion of samples being contaminated by tumor cells between the two groups, and even if patients obtained CR before harvesting.…”

Section: Discussionmentioning

confidence: 99%

Implementation of an AI-based MRD Evaluation and Prediction Model for Multiple Myeloma

Chen,

Xiong,

Wang

et al. 2024

FCIS

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With the application of hematopoietic stem cell transplantation and new drugs, the progression-free survival rate and overall survival rate of multiple myeloma have been greatly improved, but it is still considered as a kind of disease that cannot be completely cured. Many patients have disease recurrence after complete remission, which is rooted in the presence of minimal residual disease MRD in patients. Studies have shown that positive MRD is an independent adverse prognostic factor affecting survival, so MRD detection is an important indicator to judge the prognosis of patients and guide clinical treatment. At present, multipa-rameter flow cytometry (MFC), polymerase chain reaction (PCR), positron emission tomography (positron emission) Several techniques, such as PET/computer tomography (CT), have been used for MRD detection of multiple myeloma. However, there is still no cure for the disease. "IFM2013-04" four clinical studies confirmed for the first time that proteasome inhibitors (PIs) and immunomodulatory drugs, The synergism and importance of the combination of IMiDs in the treatment of MM, the large Phase 3 clinical study SWOG SO777 compared the combination of bortezomib plus lenalidomide and dexamethasone. The efficacy of VRD and D established the status of VRD first-line treatment of MM, and due to the good efficacy of CD38 monoclonal antibody in large clinical studies, combination therapy with VRD has been recommended as the first-line treatment of MM. However, to explore the clinical value and problems of applying artificial intelligence bone marrow cell recognition system Morphogo in the detection of multiple myeloma minimal residual disease (MRD).

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Section: Discussionmentioning

confidence: 99%

Implementation of an AI-based MRD Evaluation and Prediction Model for Multiple Myeloma

Chen,

Xiong,

Wang

et al. 2024

FCIS

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“…Therefore, AH<0: the intermolecular hydrogen bond prevents the water molecules from forming a hydration layer around the polymer chain, and the freedom of water molecules is reduced, so the -TAS of the system is >0. When the temperature is low, the -TAS value is small, the system AG<0, and the polymer-water system is a homogeneous solution [12]. When the temperature increases, the molecular kinetic energy increases, the hydrogen bond stability between the hydrophilic groups on the polymer and the water molecules decreases, the hydrogen bond interaction weakens, the thermal effect decreases, and the AH value decreases.…”

Section: Reversible Temperature-sensitive Transition Of Pnipammentioning

confidence: 99%

The Importance of AI Algorithm Combined with Tunable LCST Smart Polymers in Biomedical Applications

He,

Chen,

Zhou

et al. 2024

FCIS

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Smart polymers, also known as stimulus-responsive polymers or environmentally sensitive polymers, are a class of polymers that exhibit changes in their physical and chemical properties in response to various external stimuli, including small physical and chemical changes in the environment. These stimuli can trigger changes in the properties of the smart polymer, such as phase, shape, optics, mechanics, electric field, surface energy, reaction rate, permeability, and perception. Due to the biological sluggishness of conventional smart polymers, different manifestations of polymers are realized through the combination of AI technologies, including water solubility, adsorption on the surface of the carrier, or part of the cross-linked polymer system. While the definition of smart polymers can include two-phase transition processes such as glass transition and melting, the focus of research in the field of smart polymer systems is their behavior in polymer aqueous solutions, interfaces, and hydrogels. In this paper, a noteworthy smart polymer Low critical solution temperature (LCST) polymer is analyzed based on the combination of AI algorithm and deep learning. By carefully designing and modifying the structure of the polymer, the researchers can adjust the LCST to approximate the physiological temperature, making it suitable for potential biomedical applications. Looking ahead, an important development direction is the creation of LCST-type polymers that exhibit a variety of responses to different stimuli, while also improving their biodegradability. Incorporating AI into the design and modification of these polymers could facilitate the development of advanced smart materials with enhanced properties and functionality, opening up new possibilities in areas such as biotechnology, drug delivery, and response materials.

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“…Additionally, PD-L1 and NY-ESO1 are indicative markers for immunotherapy[ 150 , 174 ]. The predictive factors for favorable prognosis are shown in Table 3 [ 34 , 112 , 156 , 169 , 170 ].…”

Section: Prognosismentioning

confidence: 99%

“…A prediction model of prognosis that is based on multiple clinical indications, in combination with artificial intelligence algorithms, has been recently developed[ 34 ].…”

Section: Introductionmentioning

confidence: 99%

New trends in diagnosis and management of gallbladder carcinoma

Pavlidis,

Galanis,

Pavlidis

2024

World J Gastrointest Oncol

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Gallbladder (GB) carcinoma, although relatively rare, is the most common biliary tree cholangiocarcinoma with aggressiveness and poor prognosis. It is closely associated with cholelithiasis and long-standing large (> 3 cm) gallstones in up to 90% of cases. The other main predisposing factors for GB carcinoma include molecular factors such as mutated genes, GB wall calcification (porcelain) or mainly mucosal microcalcifications, and GB polyps ≥ 1 cm in size. Diagnosis is made by ultrasound, computed tomography (CT), and, more precisely, magnetic resonance imaging (MRI). Preoperative staging is of great importance in decision-making regarding therapeutic management. Preoperative staging is based on MRI findings, the leading technique for liver metastasis imaging, enhanced three-phase CT angiography, or magnetic resonance angiography for major vessel assessment. It is also necessary to use positron emission tomography (PET)-CT or 18F-FDG PET-MRI to more accurately detect metastases and any other occult deposits with active metabolic uptake. Staging laparoscopy may detect dissemination not otherwise found in 20%-28.6% of cases. Multimodality treatment is needed, including surgical resection, targeted therapy by biological agents according to molecular testing gene mapping, chemotherapy, radiation therapy, and immunotherapy. It is of great importance to understand the updated guidelines and current treatment options. The extent of surgical intervention depends on the disease stage, ranging from simple cholecystectomy (T1a) to extended resections and including extended cholecystectomy (T1b), with wide lymph node resection in every case or IV-V segmentectomy (T2), hepatic trisegmentectomy or major hepatectomy accompanied by hepaticojejunostomy Roux-Y, and adjacent organ resection if necessary (T3). Laparoscopic or robotic surgery shows fewer postoperative complications and equivalent oncological outcomes when compared to open surgery, but much attention must be paid to avoiding injuries. In addition to surgery, novel targeted treatment along with immunotherapy and recent improvements in radiotherapy and chemotherapy (neoadjuvant-adjuvant capecitabine, cisplatin, gemcitabine) have yielded promising results even in inoperable cases calling for palliation (T4). Thus, individualized treatment must be applied.

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Multi-clinical index classifier combined with AI algorithm model to predict the prognosis of gallbladder cancer

Cited by 6 publications

References 32 publications

Implementation of an AI-based MRD Evaluation and Prediction Model for Multiple Myeloma

Implementation of an AI-based MRD Evaluation and Prediction Model for Multiple Myeloma

The Importance of AI Algorithm Combined with Tunable LCST Smart Polymers in Biomedical Applications

New trends in diagnosis and management of gallbladder carcinoma

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